Vol. 8, No. 1, 75-92, 2009

Mapping Leaf Area Index over semi-desert and steppe biomes in Kazakhstan using satellite imagery and ground measurements
Pavel Propastin and Martin Kappas

Maps of peak seasonal leaf area index (LAI) were produced using the Normalised Difference Vegetation Index (NDVI) from SPOT VEGETATION (VEG) satellite at 1 km resolution over a large region in the semi-arid zone of Kazakhstan. Ground measurements of LAI were acquired using indirect and direct techniques across a 150ยท150 km2 large region. A Landsat Enhanced Thematic Mapper (ETM+) scene at 30 m spatial resolution was used to locate ground sites and to facilitate spatial scaling to 1 km pixels. A high-resolution LAI map retrieved from the Landsat ETM+ data was aggregated to 1 km resolution and afterwards used as reference data. The methods tested for transfer function between ETM+ LAI and SPOT-VEG were ordinary least squares (OLS) regression, non-linear regression, and reduced major axis (RMA) regression. In this paper, final maps of peak season LAI at a 1 km resolution are presented after an assessment of their accuracy using the aggregated ETM+ LAI scene. The most appropriate results were attained by RMA. Advantages and shortages of the used regression approaches were analysed and discussed. Errors were mostly caused by uncertainties in co-registration of Landsat ETM+ and SPOT-VEG images as it was demonstrated by a pixel degradation experiment. The methodology presented in this paper can serve as a basis for generation of medium- and coarse-resolution LAI satellite products for wide areas of Central Asia and Kazakhstan. The study exposed a general transferability of the developed model for LAI estimations at coarser scales. The 1000 m SPOT-VEG model has proved to be fully suitable for utilising with the SPOT-VEG data with resolution of 2 km.

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Submitted: 04 Nov 2008
Revised: 05 May 2009
Accepted: 05 May 2009
Published: 29 Jun 2009
Responsible editor: Zbigniew Bochenek

Propastin P & M Kappas, 2009. Mapping Leaf Area Index over semi-desert and steppe biomes in Kazakhstan using satellite imagery and ground measurements. EARSeL eProceedings, 8(1): 75-92


EARSeL European Association of Remote Sensing Laboratories, Strasbourg, France


BIS Library and Information System, Carl von Ossietzky University of Oldenburg


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